Dehumanization X Columbus Day/Indigenous Peoples Day

Intro

This is a data set that includes the following variables:

DH1_W_Responsible

DH2_W_Civilized

DH3_W_Moral

DH4_W_Polite

DH5_W_Childlike

DH6_W_Rational

DH7_W_Warm

DH8_W_Agentic

DH9_W_Refined

DH10_W_Lacking_Culture

DH11_W_Lacking_Self-restraint

DH12_W_Instinctual

DH13_W_Mature

DH14_W_Stoic

DH15_W_Emotionally_Responsive

DH16_W_Cold

DH17_W_Open

DH18_W_Rigid

DH19_W_Passive

DH20_W_Superficial

DH1_N_Responsible

DH2_N_Civilized

DH3_N_Moral

DH4_N_Polite

DH5_N_Childlike

DH6_N_Rational

DH7_N_Warm

DH8_N_Agentic

DH9_N_Refined

DH10_N_Lacking_Culture

DH11_N_Lacking_Self-restraint

DH12_N_Instinctual

DH13_N_Mature

DH14_N_Stoic

DH15_N_Emotionally_Responsive

DH16_N_Cold

DH17_N_Open

DH18_N_Rigid

DH19_N_Passive

DH20_N_Superficial

No_Columbus

Create_IPD

BorninUS

LengthState

FatherUSBorn

MotherUSBorn

PrimaryCaretaker

PrimaryEd

SecondCaretaker

SecondEdu

Religion

Religiosity

College

Year

Education

LibCon

SES

Race

StateNumeric

LongestRegion

SubjectNumber

EMP_comp

IMP_comp

N_Uncivilized

White_Dehumanize

Native_Dehumanize

Age

It’s primary focus is to look a bit more closely at the dehumaniztion variables and how they interact not only with one another, but with the dependent variables (Columbus Day/Indigenous Peoples’ Day (henceforth referred to as IPD)), as well as some of the demographic information gathered on the participants in this sample.

This sample comes from Study Two, so it is (if I’m not mistaken) a sample of participants gathered via mturk.

Descriptives

First, lets take a look at a descriptives table of our variables. Keep in mind that some of the variables are categorical and are more difficult to interpret in a descriptives table.

	n	mean	sd	median	se
DH1_W_Responsible	2811	61.24	21.82	62	0.4116
DH2_W_Civilized	2810	65.89	23.38	69	0.4411
DH3_W_Moral	2812	55.77	22.76	55	0.4292
DH4_W_Polite	2812	57.99	22.53	58	0.4248
DH5_W_Childlike	2809	40.76	24.49	43	0.462
DH6_W_Rational	2810	58.23	22.49	57	0.4243
DH7_W_Warm	2810	58.27	22.32	58	0.421
DH8_W_Agentic	2800	46.11	22.34	50	0.4222
DH9_W_Refined	2809	52.87	22.26	52	0.4199
DH10_W_Lacking_Culture	2808	46.34	27.71	50	0.523
DH11_W_Lacking_Self-restraint	2808	48.59	25.58	50	0.4828
DH12_W_Instinctual	2810	51.47	22.98	51	0.4335
DH13_W_Mature	2809	57.34	22.31	56	0.421
DH14_W_Stoic	2808	42.88	22.31	49	0.4209
DH15_W_Emotionally_Responsive	2812	59.98	22.77	60	0.4294
DH16_W_Cold	2810	44.74	23.99	49	0.4525
DH17_W_Open	2811	55.59	22.81	55	0.4302
DH18_W_Rigid	2807	49.6	23.27	50	0.4393
DH19_W_Passive	2810	45.16	23.21	49	0.4378
DH20_W_Superficial	2810	59.32	25.54	60	0.4818
DH1_N_Responsible	2807	63.86	21.06	64	0.3976
DH2_N_Civilized	2806	64.58	21.97	65	0.4147
DH3_N_Moral	2807	65.75	20.88	66	0.3941
DH4_N_Polite	2806	63.22	21.42	63	0.4044
DH5_N_Childlike	2802	29.22	22.26	25	0.4205
DH6_N_Rational	2807	61.61	21.17	61	0.3995
DH7_N_Warm	2804	61.05	22.07	60	0.4167
DH8_N_Agentic	2800	44.97	22.32	50	0.4219
DH9_N_Refined	2806	51.11	22.44	51	0.4235
DH10_N_Lacking_Culture	2804	23.9	23.15	17	0.4371
DH11_N_Lacking_Self-restraint	2805	34.44	23.39	33	0.4416
DH12_N_Instinctual	2807	61.3	23.48	61	0.4433
DH13_N_Mature	2806	65.15	20.69	65	0.3907
DH14_N_Stoic	2808	59.11	22.29	57	0.4206
DH15_N_Emotionally_Responsive	2806	54.38	24.29	53	0.4585
DH16_N_Cold	2805	35.19	22.8	35	0.4306
DH17_N_Open	2805	53.8	23.34	53	0.4406
DH18_N_Rigid	2807	45.41	23.89	50	0.451
DH19_N_Passive	2805	45.03	23.79	50	0.4492
DH20_N_Superficial	2805	30.41	22.87	27	0.4318
No_Columbus	2858	3.793	2.125	4	0.03976
Create_IPD	2858	4.481	1.956	4	0.03658
BorninUS*	2801	1.956	0.2041	2	0.003857
LengthState	2797	30.72	12.88	29	0.2435
FatherUSBorn*	2801	1.893	0.3352	2	0.006334
MotherUSBorn*	2801	1.882	0.3337	2	0.006306
PrimaryCaretaker*	2800	1.282	0.7601	1	0.01436
PrimaryEd	2801	2.988	1.225	3	0.02315
SecondCaretaker*	2801	2.828	1.114	3	0.02105
SecondEdu	2736	2.908	1.323	2	0.02529
Religion*	2799	2.45	1.674	2	0.03164
Religiosity	2800	3.632	2.235	4	0.04224
College*	2799	1.893	0.3094	2	0.005848
Year	300	3.363	1.631	3	0.09417
Education	2800	3.459	1.127	4	0.0213
LibCon	2799	4.401	1.713	4	0.03237
SES*	2801	1.538	0.4986	2	0.009422
Race*	2903	3.42	1.684	3	0.03125
StateNumeric*	2788	24.73	14.36	25	0.272
LongestRegion*	2789	4.425	2.104	5	0.03984
SubjectNumber	2903	1452	838.2	1452	15.56
EMP_comp	2841	3.337	1.438	3.4	0.02698
IMP_comp	2841	5.613	1.208	5.8	0.02266
N_Uncivilized	2811	2.277	1.33	2	0.02509
White_Dehumanize	2793	52.93	12.03	53.25	0.2275
Native_Dehumanize	2792	50.67	11.38	51.05	0.2153
Age	2793	38.72	13.33	35	0.2523

Correlation of Civilized & Childlike for Natives

## 
##  Pearson's product-moment correlation
## 
## data:  dfdehumanDV$N_Uncivilized and dfdehumanDV$DH5_N_Childlike
## t = 16.686, df = 2800, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2666829 0.3340519
## sample estimates:
##       cor 
## 0.3007425

At baseline the two seem to be somewhat negatively correlated with scores clustering around the Natives as Civilized and childlike.

Dependent Variables: Columbus Day X Dehumanization (Childlike)

Before we move on, let’s take a gander at the dependent variables. Does there seem to be a relationship between dehumanization and Support for getting rid of Columbus Day?

**Keep in mind while looking at this graph that low scores on the dehumanization measure mean that participants are dehumanizing the group more.

Model: Abolish Columbus Day ~ Childlike Measures

Here is the Childlike model uncentered:

## 
## Call:
## lm(formula = No_Columbus ~ DH5_N_Childlike + DH5_W_Childlike + 
##     DH5_N_Childlike * DH5_W_Childlike, data = dv4cor)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0529 -1.8904  0.0164  1.9077  4.0183 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      3.282e+00  1.014e-01  32.350  < 2e-16 ***
## DH5_N_Childlike                 -5.454e-03  3.656e-03  -1.492    0.136    
## DH5_W_Childlike                  1.771e-02  2.295e-03   7.719 1.62e-14 ***
## DH5_N_Childlike:DH5_W_Childlike -4.217e-05  6.721e-05  -0.627    0.530    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.093 on 2798 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.03255,    Adjusted R-squared:  0.03151 
## F-statistic: 31.38 on 3 and 2798 DF,  p-value: < 2.2e-16

…and here it is centered:

## 
## Call:
## lm(formula = No_Columbus ~ scale(DH5_N_Childlike, scale = F) + 
##     scale(DH5_W_Childlike, scale = F) + scale(DH5_N_Childlike, 
##     scale = F) * scale(DH5_W_Childlike, scale = F), data = dv4cor)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0529 -1.8904  0.0164  1.9077  4.0183 
## 
## Coefficients:
##                                                                       Estimate
## (Intercept)                                                          3.794e+00
## scale(DH5_N_Childlike, scale = F)                                   -7.172e-03
## scale(DH5_W_Childlike, scale = F)                                    1.648e-02
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) -4.217e-05
##                                                                     Std. Error
## (Intercept)                                                          4.171e-02
## scale(DH5_N_Childlike, scale = F)                                    1.944e-03
## scale(DH5_W_Childlike, scale = F)                                    1.793e-03
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F)  6.721e-05
##                                                                     t value
## (Intercept)                                                          90.962
## scale(DH5_N_Childlike, scale = F)                                    -3.689
## scale(DH5_W_Childlike, scale = F)                                     9.189
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F)  -0.627
##                                                                     Pr(>|t|)
## (Intercept)                                                          < 2e-16
## scale(DH5_N_Childlike, scale = F)                                   0.000229
## scale(DH5_W_Childlike, scale = F)                                    < 2e-16
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) 0.530454
##                                                                        
## (Intercept)                                                         ***
## scale(DH5_N_Childlike, scale = F)                                   ***
## scale(DH5_W_Childlike, scale = F)                                   ***
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F)    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.093 on 2798 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.03255,    Adjusted R-squared:  0.03151 
## F-statistic: 31.38 on 3 and 2798 DF,  p-value: < 2.2e-16

Dependent Variables: Columbus Day X Dehumanization (Civilized)

Before we move on, let’s take a gander at the dependent variables. Does there seem to be a relationship between dehumanization and Support for getting rid of Columbus Day?

**Keep in mind while looking at this graph that low scores on the dehumanization measure mean that participants are dehumanizing the group more.

Model: Abolish Columbus Day ~ Civilized Measures

Here is the Civilized model uncentered:

## 
## Call:
## lm(formula = No_Columbus ~ N_Uncivilized, data = dv4cor2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0475 -1.8429 -0.0475  1.9525  4.1797 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    4.25201    0.07889  53.896  < 2e-16 ***
## N_Uncivilized -0.20453    0.02991  -6.838 9.85e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.11 on 2809 degrees of freedom
##   (92 observations deleted due to missingness)
## Multiple R-squared:  0.01637,    Adjusted R-squared:  0.01602 
## F-statistic: 46.75 on 1 and 2809 DF,  p-value: 9.846e-12

…and here it is centered:

## 
## Call:
## lm(formula = No_Columbus ~ scale(N_Uncivilized, scale = F), data = dv4cor2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0475 -1.8429 -0.0475  1.9525  4.1797 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      3.78620    0.03979  95.158  < 2e-16 ***
## scale(N_Uncivilized, scale = F) -0.20453    0.02991  -6.838 9.85e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.11 on 2809 degrees of freedom
##   (92 observations deleted due to missingness)
## Multiple R-squared:  0.01637,    Adjusted R-squared:  0.01602 
## F-statistic: 46.75 on 1 and 2809 DF,  p-value: 9.846e-12

Dependent Variables: Indigenous Peoples’ Day X Dehumanization (Childlike)

Before we move on, let’s take a gander at the dependent variables. Does there seem to be a relationship between dehumanization and Support for establishing an Indigenous Peoples’ Day?

**Keep in mind while looking at this graph that low scores on the dehumanization measure mean that participants are dehumanizing the group more.

Model: Indigenous Peoples’ Day ~ Childlike Measures

Here is the Childlike model uncentered:

## 
## Call:
## lm(formula = Create_IPD ~ DH5_N_Childlike + DH5_W_Childlike + 
##     DH5_N_Childlike * DH5_W_Childlike, data = dv5cor)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4267 -1.2536 -0.1019  1.6597  3.3779 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      4.253e+00  9.372e-02  45.382  < 2e-16 ***
## DH5_N_Childlike                 -1.148e-02  3.378e-03  -3.397 0.000691 ***
## DH5_W_Childlike                  1.173e-02  2.120e-03   5.535 3.41e-08 ***
## DH5_N_Childlike:DH5_W_Childlike  5.913e-05  6.209e-05   0.952 0.341053    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.933 on 2798 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.02507,    Adjusted R-squared:  0.02403 
## F-statistic: 23.99 on 3 and 2798 DF,  p-value: 2.523e-15

…and here it is centered:

## 
## Call:
## lm(formula = Create_IPD ~ scale(DH5_N_Childlike, scale = F) + 
##     scale(DH5_W_Childlike, scale = F) + scale(DH5_N_Childlike, 
##     scale = F) * scale(DH5_W_Childlike, scale = F), data = dv5cor)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4267 -1.2536 -0.1019  1.6597  3.3779 
## 
## Coefficients:
##                                                                       Estimate
## (Intercept)                                                          4.467e+00
## scale(DH5_N_Childlike, scale = F)                                   -9.066e-03
## scale(DH5_W_Childlike, scale = F)                                    1.346e-02
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F)  5.913e-05
##                                                                     Std. Error
## (Intercept)                                                          3.854e-02
## scale(DH5_N_Childlike, scale = F)                                    1.796e-03
## scale(DH5_W_Childlike, scale = F)                                    1.657e-03
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F)  6.209e-05
##                                                                     t value
## (Intercept)                                                         115.909
## scale(DH5_N_Childlike, scale = F)                                    -5.047
## scale(DH5_W_Childlike, scale = F)                                     8.125
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F)   0.952
##                                                                     Pr(>|t|)
## (Intercept)                                                          < 2e-16
## scale(DH5_N_Childlike, scale = F)                                   4.77e-07
## scale(DH5_W_Childlike, scale = F)                                   6.65e-16
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F)    0.341
##                                                                        
## (Intercept)                                                         ***
## scale(DH5_N_Childlike, scale = F)                                   ***
## scale(DH5_W_Childlike, scale = F)                                   ***
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F)    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.933 on 2798 degrees of freedom
##   (101 observations deleted due to missingness)
## Multiple R-squared:  0.02507,    Adjusted R-squared:  0.02403 
## F-statistic: 23.99 on 3 and 2798 DF,  p-value: 2.523e-15

Dependent Variables: Indigenous Peoples’ Day X Dehumanization (Civilized)

Before we move on, let’s take a gander at the dependent variables. Does there seem to be a relationship between dehumanization and Support for establishing an Indegenous Peoples’ Day?

**Keep in mind while looking at this graph that low scores on the dehumanization measure mean that participants are dehumanizing the group more.

Model: Indigenous Peoples’ Day ~ Civilized Measures

Here is the Civilized model uncentered:

## 
## Call:
## lm(formula = Create_IPD ~ N_Uncivilized, data = dv5cor2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8094 -1.0323  0.1906  1.4497  3.7447 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    5.06837    0.07205  70.346   <2e-16 ***
## N_Uncivilized -0.25901    0.02732  -9.481   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.927 on 2809 degrees of freedom
##   (92 observations deleted due to missingness)
## Multiple R-squared:  0.03101,    Adjusted R-squared:  0.03067 
## F-statistic:  89.9 on 1 and 2809 DF,  p-value: < 2.2e-16

…and here it is centered:

## 
## Call:
## lm(formula = Create_IPD ~ scale(N_Uncivilized, scale = F), data = dv5cor2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8094 -1.0323  0.1906  1.4497  3.7447 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      4.47848    0.03634 123.249   <2e-16 ***
## scale(N_Uncivilized, scale = F) -0.25901    0.02732  -9.481   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.927 on 2809 degrees of freedom
##   (92 observations deleted due to missingness)
## Multiple R-squared:  0.03101,    Adjusted R-squared:  0.03067 
## F-statistic:  89.9 on 1 and 2809 DF,  p-value: < 2.2e-16

Dependent Variables: Side by Sides

We can also look at both of these groups side by side:

and…

Dependent Variables: Digging a Little Deeper

For Civilized

## 
## Call:
## lm(formula = Rating ~ N_Uncivilized + Holiday_Support + N_Uncivilized:Holiday_Support, 
##     data = dv4and5cor2.lng.cen)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8094 -1.8429 -0.0286  1.9525  4.1797 
## 
## Coefficients:
##                                          Estimate Std. Error t value
## (Intercept)                               4.47848    0.03810 117.540
## N_Uncivilized                            -0.25901    0.02864  -9.042
## Holiday_SupportNo_Columbus               -0.69228    0.05388 -12.848
## N_Uncivilized:Holiday_SupportNo_Columbus  0.05448    0.04051   1.345
##                                          Pr(>|t|)    
## (Intercept)                                <2e-16 ***
## N_Uncivilized                              <2e-16 ***
## Holiday_SupportNo_Columbus                 <2e-16 ***
## N_Uncivilized:Holiday_SupportNo_Columbus    0.179    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.02 on 5618 degrees of freedom
##   (184 observations deleted due to missingness)
## Multiple R-squared:  0.05034,    Adjusted R-squared:  0.04983 
## F-statistic: 99.27 on 3 and 5618 DF,  p-value: < 2.2e-16

For Childlike

## 
## Call:
## lm(formula = Rating ~ DH5_N_Childlike + Holiday_Support + DH5_N_Childlike:Holiday_Support, 
##     data = dv4and5cor.lng.cen)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5808 -1.7851  0.2009  1.6018  3.2646 
## 
## Coefficients:
##                                             Estimate Std. Error t value
## (Intercept)                                 4.478230   0.038595 116.031
## DH5_N_Childlike                            -0.003512   0.001734  -2.025
## Holiday_SupportNo_Columbus                 -0.692719   0.054582 -12.691
## DH5_N_Childlike:Holiday_SupportNo_Columbus  0.002804   0.002453   1.143
##                                            Pr(>|t|)    
## (Intercept)                                  <2e-16 ***
## DH5_N_Childlike                              0.0429 *  
## Holiday_SupportNo_Columbus                   <2e-16 ***
## DH5_N_Childlike:Holiday_SupportNo_Columbus   0.2530    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.043 on 5600 degrees of freedom
##   (202 observations deleted due to missingness)
## Multiple R-squared:  0.02868,    Adjusted R-squared:  0.02816 
## F-statistic: 55.11 on 3 and 5600 DF,  p-value: < 2.2e-16

Exploratory Analysis

Let’s dig into some of the demographic information in this subset of the data.

Race

First let’s look at the frequency of our different racial groups

Asian	Black	White	Latino	Middle Eastern	Native	Other	Multiracial
139	224	2097	106	4	17	24	292

Since our data skews toward Asian, Black, White, and Latino let’s use these groups in future analysis.

Race X Support for Abolishing Columbus Day

First off, let’s look at how our different racial groups answered the question about Columbus Day.

Race X Support for Creating an Indigenous Peoples’ Day

Next let’s see how these groups answered the question about Indigenous Peoples’ Day

Race X Measures of Dehumanization: Civilized

For the next set of graphs let’s revisit the question of dehumanization. What we’re interested in here is whether or not responses to the questions of dehumanization differed by racial group.

Race	N	N_Uncivilized	sd	se	ci
Asian	139	2.748	1.373	0.1165	0.2303
Black	223	2.251	1.461	0.09784	0.1928
White	2096	2.257	1.286	0.02809	0.0551
Latino	106	2.575	1.615	0.1569	0.311

##               Df Sum Sq Mean Sq F value   Pr(>F)    
## Race           3     40  13.464   7.704 4.01e-05 ***
## Residuals   2560   4474   1.748                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingness

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = N_Uncivilized ~ Race, data = dfdehumanDV.race)
## 
## $Race
##                      diff         lwr        upr     p adj
## Black-Asian  -0.497080363 -0.86434038 -0.1298203 0.0028715
## White-Asian  -0.491522050 -0.78917824 -0.1938659 0.0001329
## Latino-Asian -0.172729741 -0.61095954  0.2655001 0.7416893
## White-Black   0.005558313 -0.23381818  0.2449348 0.9999237
## Latino-Black  0.324350622 -0.07658236  0.7252836 0.1599987
## Latino-White  0.318792309 -0.01953673  0.6571214 0.0731740

This analysis is done via One way ANOVA using ratings of how civilized Natives are as the DV

Race X Measures of Dehumanization: Childlike

Now let’s look at those same tables and bar graphs for ratings of how childlike Natives are:

Race	N	DH5_N_Childlike	sd	se	ci
Asian	139	35.21	22.16	1.879	3.716
Black	222	23.43	23.63	1.586	3.125
White	2091	29.22	21.65	0.4735	0.9286
Latino	106	32.21	26.81	2.604	5.163

##               Df  Sum Sq Mean Sq F value   Pr(>F)    
## Race           3   13372    4457   9.134 5.19e-06 ***
## Residuals   2554 1246388     488                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 8 observations deleted due to missingness

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = DH5_N_Childlike ~ Race, data = dfdehumanDV.race)
## 
## $Race
##                    diff        lwr       upr     p adj
## Black-Asian  -11.780705 -17.923150 -5.638260 0.0000052
## White-Asian   -5.984817 -10.959204 -1.010429 0.0107867
## Latino-Asian  -3.001086 -10.324175  4.322003 0.7178329
## White-Black    5.795888   1.787167  9.804610 0.0011799
## Latino-Black   8.779619   2.074923 15.484315 0.0042992
## Latino-White   2.983731  -2.670280  8.637742 0.5268555

This analysis is done via One way ANOVA using Dehumanization of Natives as the DV

Dehumanization and Holidays X Race: Civilized

Now, we’ll revisit the relationship between dehumanizing whites and support for the two holidays, this time broken down by race.

First Columbus Day:

Then Indigenous Peoples’ Day:

Dehumanization and Holidays X Race: Childlike

Now, we’ll revisit the relationship between dehumanizing whites and support for the two holidays, this time broken down by race.

First Columbus Day:

Then Indigenous Peoples’ Day:

Stereotypes in detail

Now we’re going to take the opportunity to drill down a bit into the question of Native Stereotypes.

This time we’re going to pull the stereotype of Natives as Childlike as well as the stereotype of Natives as Uncivilized.

The way I’m going to do this is to pull both measures out of the dehumanization composite score and look at their effects on our DVs separately. To test this statistically we’ll place them into linear models together with the composite.

Let’s begin!

Childlike

This first graph will show us regression lines for each of our 4 measures of dehumanization and their relationship with Support for abolishing Columbus Day

##                   vars    n    mean     sd  median trimmed     mad min
## SubjectNumber        1 2903 1452.00 838.17 1452.00 1452.00 1076.37   1
## No_Columbus          2 2858    3.79   2.13    4.00    3.74    2.97   1
## White_Dehumanize     3 2793   52.93  12.03   53.25   53.60    8.52   0
## Native_Dehumanize    4 2792   51.09  11.58   51.50   51.72    7.91   0
## DH5_W_Childlike      5 2809   40.76  24.49   43.00   39.98   26.69   0
## DH5_N_Childlike      6 2802   29.22  22.26   25.00   27.68   28.17   0
##                       max   range  skew kurtosis    se
## SubjectNumber     2903.00 2902.00  0.00    -1.20 15.56
## No_Columbus          7.00    6.00  0.17    -1.31  0.04
## White_Dehumanize    99.95   99.95 -0.87     3.45  0.23
## Native_Dehumanize  100.00  100.00 -0.87     4.02  0.22
## DH5_W_Childlike    100.00  100.00  0.21    -0.55  0.46
## DH5_N_Childlike    100.00  100.00  0.54    -0.43  0.42

## 
## Call:
## lm(formula = scale(No_Columbus, scale = F) ~ White_Dehumanize + 
##     Native_Dehumanize + DH5_W_Childlike + DH5_N_Childlike, data = dvstereotypes.child.c)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8633 -1.8083 -0.0807  1.7581  4.6669 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.783234   0.188753   4.150 3.43e-05 ***
## White_Dehumanize  -0.053827   0.004709 -11.431  < 2e-16 ***
## Native_Dehumanize  0.027957   0.004910   5.694 1.37e-08 ***
## DH5_W_Childlike    0.018252   0.001726  10.575  < 2e-16 ***
## DH5_N_Childlike   -0.003985   0.001913  -2.082   0.0374 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.041 on 2781 degrees of freedom
##   (117 observations deleted due to missingness)
## Multiple R-squared:  0.07825,    Adjusted R-squared:  0.07693 
## F-statistic: 59.03 on 4 and 2781 DF,  p-value: < 2.2e-16

And we’ll do one for Indigenous Peoples’ Day as well:

##                   vars    n    mean     sd  median trimmed     mad min
## SubjectNumber        1 2903 1452.00 838.17 1452.00 1452.00 1076.37   1
## Create_IPD           2 2858    4.48   1.96    4.00    4.60    2.97   1
## White_Dehumanize     3 2793   52.93  12.03   53.25   53.60    8.52   0
## Native_Dehumanize    4 2792   51.09  11.58   51.50   51.72    7.91   0
## DH5_W_Childlike      5 2809   40.76  24.49   43.00   39.98   26.69   0
## DH5_N_Childlike      6 2802   29.22  22.26   25.00   27.68   28.17   0
##                       max   range  skew kurtosis    se
## SubjectNumber     2903.00 2902.00  0.00    -1.20 15.56
## Create_IPD           7.00    6.00 -0.35    -0.97  0.04
## White_Dehumanize    99.95   99.95 -0.87     3.45  0.23
## Native_Dehumanize  100.00  100.00 -0.87     4.02  0.22
## DH5_W_Childlike    100.00  100.00  0.21    -0.55  0.46
## DH5_N_Childlike    100.00  100.00  0.54    -0.43  0.42

## 
## Call:
## lm(formula = scale(Create_IPD, scale = F) ~ White_Dehumanize + 
##     Native_Dehumanize + DH5_W_Childlike + DH5_N_Childlike, data = dvstereotypes.child.i)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5164 -1.3058  0.0292  1.6081  3.8440 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -0.033044   0.176129  -0.188    0.851    
## White_Dehumanize  -0.038655   0.004394  -8.798  < 2e-16 ***
## Native_Dehumanize  0.034383   0.004581   7.505 8.24e-14 ***
## DH5_W_Childlike    0.013216   0.001610   8.206 3.46e-16 ***
## DH5_N_Childlike   -0.007674   0.001785  -4.298 1.78e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.905 on 2781 degrees of freedom
##   (117 observations deleted due to missingness)
## Multiple R-squared:  0.05297,    Adjusted R-squared:  0.05161 
## F-statistic: 38.89 on 4 and 2781 DF,  p-value: < 2.2e-16

Civilized

Now we’ll take a look at stereotypes about being civilized using the same methods as before:

##                   vars    n    mean     sd  median trimmed     mad min
## SubjectNumber        1 2903 1452.00 838.17 1452.00 1452.00 1076.37   1
## No_Columbus          2 2858    3.79   2.13    4.00    3.74    2.97   1
## White_Dehumanize     3 2793   52.93  12.03   53.25   53.60    8.52   0
## Native_Dehumanize    4 2792   51.09  11.58   51.50   51.72    7.91   0
## DH2_W_Civilized      5 2810   65.89  23.38   69.00   67.81   25.20   0
## N_Uncivilized        6 2811    2.28   1.33    2.00    2.10    1.48   1
##                       max   range  skew kurtosis    se
## SubjectNumber     2903.00 2902.00  0.00    -1.20 15.56
## No_Columbus          7.00    6.00  0.17    -1.31  0.04
## White_Dehumanize    99.95   99.95 -0.87     3.45  0.23
## Native_Dehumanize  100.00  100.00 -0.87     4.02  0.22
## DH2_W_Civilized    100.00  100.00 -0.69     0.20  0.44
## N_Uncivilized        7.00    6.00  1.01     0.39  0.03

## 
## Call:
## lm(formula = scale(No_Columbus, scale = F) ~ White_Dehumanize + 
##     Native_Dehumanize + DH2_W_Civilized + N_Uncivilized, data = dvstereotypes.civil.c)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8540 -1.7106 -0.1154  1.6579  4.9535 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        1.616558   0.198675   8.137 6.06e-16 ***
## White_Dehumanize  -0.007693   0.005631  -1.366    0.172    
## Native_Dehumanize  0.020416   0.004884   4.180 3.00e-05 ***
## DH2_W_Civilized   -0.027049   0.002142 -12.629  < 2e-16 ***
## N_Uncivilized     -0.210592   0.029076  -7.243 5.66e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.012 on 2781 degrees of freedom
##   (117 observations deleted due to missingness)
## Multiple R-squared:  0.1045, Adjusted R-squared:  0.1032 
## F-statistic: 81.15 on 4 and 2781 DF,  p-value: < 2.2e-16

And we’ll do one for Indigenous Peoples’ Day as well:

##                   vars    n    mean     sd  median trimmed     mad min
## SubjectNumber        1 2903 1452.00 838.17 1452.00 1452.00 1076.37   1
## Create_IPD           2 2858    4.48   1.96    4.00    4.60    2.97   1
## White_Dehumanize     3 2793   52.93  12.03   53.25   53.60    8.52   0
## Native_Dehumanize    4 2792   51.09  11.58   51.50   51.72    7.91   0
## DH2_W_Civilized      5 2810   65.89  23.38   69.00   67.81   25.20   0
## N_Uncivilized        6 2811    2.28   1.33    2.00    2.10    1.48   1
##                       max   range  skew kurtosis    se
## SubjectNumber     2903.00 2902.00  0.00    -1.20 15.56
## Create_IPD           7.00    6.00 -0.35    -0.97  0.04
## White_Dehumanize    99.95   99.95 -0.87     3.45  0.23
## Native_Dehumanize  100.00  100.00 -0.87     4.02  0.22
## DH2_W_Civilized    100.00  100.00 -0.69     0.20  0.44
## N_Uncivilized        7.00    6.00  1.01     0.39  0.03

## 
## Call:
## lm(formula = scale(Create_IPD, scale = F) ~ White_Dehumanize + 
##     Native_Dehumanize + DH2_W_Civilized + N_Uncivilized, data = dvstereotypes.civil.i)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -5.395 -1.159  0.123  1.544  4.267 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.810852   0.185547   4.370 1.29e-05 ***
## White_Dehumanize  -0.008018   0.005259  -1.525    0.127    
## Native_Dehumanize  0.025919   0.004561   5.682 1.47e-08 ***
## DH2_W_Civilized   -0.017364   0.002000  -8.680  < 2e-16 ***
## N_Uncivilized     -0.252216   0.027155  -9.288  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.879 on 2781 degrees of freedom
##   (117 observations deleted due to missingness)
## Multiple R-squared:  0.07836,    Adjusted R-squared:  0.07703 
## F-statistic: 59.11 on 4 and 2781 DF,  p-value: < 2.2e-16

The story here seems to be that in the middle, when we’re talking about the average level of dehumanization, people are more or less treating the civilized question like they treat the other measures of dehumanization. However, when it comes to perceptions of Whites and Natives as childlike something else starts happening, particularly at the extremes. Whether there is a story there for These two measure in particular is difficult to say.

Childlike and Civilized by Race

Let’s look at these measures broken down by race and then by education level:

##                Df  Sum Sq Mean Sq F value   Pr(>F)    
## Group           5 6503147 1300629 4167.00  < 2e-16 ***
## Race            3   15830    5277   16.91 5.85e-11 ***
## Group:Race     15   35861    2391    7.66  < 2e-16 ***
## Residuals   15320 4781767     312                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 52 observations deleted due to missingness

And in case we haven’t done it, here’s the measure’s side by side without separating for race:

##                Df  Sum Sq Mean Sq F value Pr(>F)    
## Group           5 6503147 1300629    4127 <2e-16 ***
## Residuals   15338 4833458     315                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 52 observations deleted due to missingness

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Rating ~ Group, data = dvstereotypes.race.lng)
## 
## $Group
##                                          diff        lwr         upr
## Native_Dehumanize-White_Dehumanize  -1.938569  -3.355904  -0.5212349
## DH2_W_Civilized-White_Dehumanize    13.523152  12.107754  14.9385500
## N_Uncivilized-White_Dehumanize     -50.761396 -52.176656 -49.3461354
## DH5_W_Childlike-White_Dehumanize   -12.558589 -13.974124 -11.1430529
## DH5_N_Childlike-White_Dehumanize   -23.887754 -25.303841 -22.4716664
## DH2_W_Civilized-Native_Dehumanize   15.461721  14.046463  16.8769800
## N_Uncivilized-Native_Dehumanize    -48.822827 -50.237948 -47.4077054
## DH5_W_Childlike-Native_Dehumanize  -10.620019 -12.035416  -9.2046229
## DH5_N_Childlike-Native_Dehumanize  -21.949185 -23.365133 -20.5332363
## N_Uncivilized-DH2_W_Civilized      -64.284548 -65.697729 -62.8713662
## DH5_W_Childlike-DH2_W_Civilized    -26.081741 -27.495198 -24.6682833
## DH5_N_Childlike-DH2_W_Civilized    -37.410906 -38.824916 -35.9968959
## DH5_W_Childlike-N_Uncivilized       38.202807  36.789488  39.6161267
## DH5_N_Childlike-N_Uncivilized       26.873642  25.459770  28.2875141
## DH5_N_Childlike-DH5_W_Childlike    -11.329165 -12.743313  -9.9150175
##                                        p adj
## Native_Dehumanize-White_Dehumanize 0.0013639
## DH2_W_Civilized-White_Dehumanize   0.0000000
## N_Uncivilized-White_Dehumanize     0.0000000
## DH5_W_Childlike-White_Dehumanize   0.0000000
## DH5_N_Childlike-White_Dehumanize   0.0000000
## DH2_W_Civilized-Native_Dehumanize  0.0000000
## N_Uncivilized-Native_Dehumanize    0.0000000
## DH5_W_Childlike-Native_Dehumanize  0.0000000
## DH5_N_Childlike-Native_Dehumanize  0.0000000
## N_Uncivilized-DH2_W_Civilized      0.0000000
## DH5_W_Childlike-DH2_W_Civilized    0.0000000
## DH5_N_Childlike-DH2_W_Civilized    0.0000000
## DH5_W_Childlike-N_Uncivilized      0.0000000
## DH5_N_Childlike-N_Uncivilized      0.0000000
## DH5_N_Childlike-DH5_W_Childlike    0.0000000

What we end up with is several possible stories. The big picture seems to be that in some cases it is the measure that is differentiating and other times the race of the participant seems to be driving differences, particularly in our civilized and childlike measures.

Childlike and Civilized by Education

Now, let’s take a bit of a left turn and introduce another wrinkle into this analysis. How do these things break down by education level. For the sake of argument we’ll look just at college educated versus those who are not college educated.

##                  Df  Sum Sq Mean Sq  F value   Pr(>F)    
## Group             5 6979166 1395833 4369.986  < 2e-16 ***
## College           1    1310    1310    4.101   0.0429 *  
## Group:College     5   21309    4262   13.343 5.25e-13 ***
## Residuals     16717 5339638     319                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 689 observations deleted due to missingness

A simple interaction plot will show us these effects in a different form:

Miscellaneous

This will be where we put other questions and answers that we have.

External Motivation to Appear Non-Prejudiced

• EMP_comp:

  Table continues below

  	vars	n	mean	sd	median
  DH1_W_Responsible	1	2811	61.24	21.82	62
  DH2_W_Civilized	2	2810	65.89	23.38	69
  DH3_W_Moral	3	2812	55.77	22.76	55
  DH4_W_Polite	4	2812	57.99	22.53	58
  DH5_W_Childlike	5	2809	40.76	24.49	43
  DH6_W_Rational	6	2810	58.23	22.49	57
  DH7_W_Warm	7	2810	58.27	22.32	58
  DH8_W_Agentic	8	2800	46.11	22.34	50
  DH9_W_Refined	9	2809	52.87	22.26	52
  DH10_W_Lacking_Culture	10	2808	46.34	27.71	50
  DH11_W_Lacking_Self-restraint	11	2808	48.59	25.58	50
  DH12_W_Instinctual	12	2810	51.47	22.98	51
  DH13_W_Mature	13	2809	57.34	22.31	56
  DH14_W_Stoic	14	2808	42.88	22.31	49
  DH15_W_Emotionally_Responsive	15	2812	59.98	22.77	60
  DH16_W_Cold	16	2810	44.74	23.99	49
  DH17_W_Open	17	2811	55.59	22.81	55
  DH18_W_Rigid	18	2807	49.6	23.27	50
  DH19_W_Passive	19	2810	45.16	23.21	49
  DH20_W_Superficial	20	2810	59.32	25.54	60
  DH1_N_Responsible	21	2807	63.86	21.06	64
  N_Uncivilized	22	2811	2.277	1.33	2
  DH3_N_Moral	23	2807	65.75	20.88	66
  DH4_N_Polite	24	2806	63.22	21.42	63
  DH5_N_Childlike	25	2802	29.22	22.26	25
  DH6_N_Rational	26	2807	61.61	21.17	61
  DH7_N_Warm	27	2804	61.05	22.07	60
  DH8_N_Agentic	28	2800	44.97	22.32	50
  DH9_N_Refined	29	2806	51.11	22.44	51
  DH10_N_Lacking_Culture	30	2804	23.9	23.15	17
  DH11_N_Lacking_Self-restraint	31	2805	34.44	23.39	33
  DH12_N_Instinctual	32	2807	61.3	23.48	61
  DH13_N_Mature	33	2806	65.15	20.69	65
  DH14_N_Stoic	34	2808	59.11	22.29	57
  DH15_N_Emotionally_Responsive	35	2806	54.38	24.29	53
  DH16_N_Cold	36	2805	35.19	22.8	35
  DH17_N_Open	37	2805	53.8	23.34	53
  DH18_N_Rigid	38	2807	45.41	23.89	50
  DH19_N_Passive	39	2805	45.03	23.79	50
  DH20_N_Superficial	40	2805	30.41	22.87	27
  EMP*	41	2903	1	0	1
  Mean Score	42	2841	3.337	1.438	3.4

  Table continues below

  	trimmed	mad	min	max	range
  DH1_W_Responsible	62.64	19.27	0	100	100
  DH2_W_Civilized	67.81	25.2	0	100	100
  DH3_W_Moral	56.82	22.24	0	100	100
  DH4_W_Polite	59.29	20.76	0	100	100
  DH5_W_Childlike	39.98	26.69	0	100	100
  DH6_W_Rational	59.45	20.76	0	100	100
  DH7_W_Warm	59.58	19.27	0	100	100
  DH8_W_Agentic	46.85	11.86	0	100	100
  DH9_W_Refined	53.56	20.76	0	100	100
  DH10_W_Lacking_Culture	45.92	32.62	0	100	100
  DH11_W_Lacking_Self-restraint	48.69	28.17	0	100	100
  DH12_W_Instinctual	52.05	20.76	0	100	100
  DH13_W_Mature	58.44	20.76	0	100	100
  DH14_W_Stoic	42.9	20.76	0	100	100
  DH15_W_Emotionally_Responsive	61.27	20.76	0	100	100
  DH16_W_Cold	44.43	25.2	0	100	100
  DH17_W_Open	56.57	22.24	0	100	100
  DH18_W_Rigid	50.06	22.24	0	100	100
  DH19_W_Passive	45.16	23.72	0	100	100
  DH20_W_Superficial	60.99	25.2	0	100	100
  DH1_N_Responsible	64.78	20.76	0	100	100
  N_Uncivilized	2.105	1.483	1	7	6
  DH3_N_Moral	66.67	22.24	0	100	100
  DH4_N_Polite	64.15	19.27	0	100	100
  DH5_N_Childlike	27.68	28.17	0	100	100
  DH6_N_Rational	62.6	17.79	0	100	100
  DH7_N_Warm	61.99	20.76	0	100	100
  DH8_N_Agentic	45.62	11.86	0	100	100
  DH9_N_Refined	51.46	19.27	0	100	100
  DH10_N_Lacking_Culture	20.99	22.24	0	100	100
  DH11_N_Lacking_Self-restraint	33.22	26.69	0	100	100
  DH12_N_Instinctual	62.82	20.76	0	100	100
  DH13_N_Mature	66.08	22.24	0	100	100
  DH14_N_Stoic	60.12	20.76	0	100	100
  DH15_N_Emotionally_Responsive	55.39	22.24	0	100	100
  DH16_N_Cold	34.35	25.2	0	100	100
  DH17_N_Open	54.26	22.24	0	100	100
  DH18_N_Rigid	45.67	23.72	0	100	100
  DH19_N_Passive	44.94	22.24	0	100	100
  DH20_N_Superficial	28.75	29.65	0	100	100
  EMP*	1	0	1	1	0
  Mean Score	3.306	1.483	1	7	6

  	skew	kurtosis	se
  DH1_W_Responsible	-0.5742	0.2696	0.4116
  DH2_W_Civilized	-0.6889	0.2007	0.4411
  DH3_W_Moral	-0.3719	-0.1342	0.4292
  DH4_W_Polite	-0.4732	0.009527	0.4248
  DH5_W_Childlike	0.2092	-0.5542	0.462
  DH6_W_Rational	-0.4488	0.001843	0.4243
  DH7_W_Warm	-0.4832	0.07056	0.421
  DH8_W_Agentic	-0.3413	0.1309	0.4222
  DH9_W_Refined	-0.2509	-0.1186	0.4199
  DH10_W_Lacking_Culture	0.07817	-0.8826	0.523
  DH11_W_Lacking_Self-restraint	-0.04029	-0.7017	0.4828
  DH12_W_Instinctual	-0.2033	-0.2257	0.4335
  DH13_W_Mature	-0.4135	0.04243	0.421
  DH14_W_Stoic	-0.04162	-0.298	0.4209
  DH15_W_Emotionally_Responsive	-0.4721	0.002383	0.4294
  DH16_W_Cold	0.09141	-0.5112	0.4525
  DH17_W_Open	-0.3409	-0.1675	0.4302
  DH18_W_Rigid	-0.1571	-0.3567	0.4393
  DH19_W_Passive	0.002147	-0.3755	0.4378
  DH20_W_Superficial	-0.4857	-0.3722	0.4818
  DH1_N_Responsible	-0.4596	0.317	0.3976
  N_Uncivilized	1.006	0.3926	0.02509
  DH3_N_Moral	-0.527	0.4136	0.3941
  DH4_N_Polite	-0.4299	0.2116	0.4044
  DH5_N_Childlike	0.5351	-0.4322	0.4205
  DH6_N_Rational	-0.4534	0.336	0.3995
  DH7_N_Warm	-0.3667	0.02378	0.4167
  DH8_N_Agentic	-0.3395	-0.007707	0.4219
  DH9_N_Refined	-0.1243	-0.06897	0.4235
  DH10_N_Lacking_Culture	0.978	0.2417	0.4371
  DH11_N_Lacking_Self-restraint	0.3627	-0.4915	0.4416
  DH12_N_Instinctual	-0.5158	0.04848	0.4433
  DH13_N_Mature	-0.5208	0.4368	0.3907
  DH14_N_Stoic	-0.3809	0.0361	0.4206
  DH15_N_Emotionally_Responsive	-0.3216	-0.2435	0.4585
  DH16_N_Cold	0.2317	-0.6249	0.4306
  DH17_N_Open	-0.1702	-0.3199	0.4406
  DH18_N_Rigid	-0.1095	-0.5238	0.451
  DH19_N_Passive	0.006644	-0.4618	0.4492
  DH20_N_Superficial	0.543	-0.3894	0.4318
  EMP*	NA	NA	0
  Mean Score	0.1451	-0.5044	0.02698